compare outliers from different comparison cohorts 2024.02.16 15.52.13

version 2023.11.27_11.25.57 adds per-patient analysis version 2024.02.16_15.52.13 adds a table to add to appendix tables

underscore_to_space <- function(x) str_replace_all(x, "_", " ")
underscore_to_space_initial_cap <- function(x) str_replace_all(x, "_", " ") %>% str_to_sentence()

outliers <- read_tsv("../input_data/druggable_outliers_from_treehouse_and_other_cohorts_2023_11_09-13_46_32_2023.tsv") %>%
  mutate(high_level_cohort = ifelse(str_detect(comparison_cohort, "Treehouse"),
                                    "Treehouse",
                                    comparison_cohort))

## Rows: 287 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): Sample_ID, comparison_cohort, gene, donor_ID
## lgl (1): pathway_support
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

n_distinct(outliers$Sample_ID)       

## [1] 34

n_distinct(outliers$donor_ID)

## [1] 32

Define cohort codes

cohort_codes <- tibble(
  cohort_name = 
    c("PEDAYA", "TCGA", "TH03_TH34", "Treehouse_pc", "Treehouse_pd"),
  cohort_code = 
    c("P", "T", "S", "C", "D"))

Tile plot of all outliers

ggplot(outliers) +
  geom_tile(aes(x=comparison_cohort,
                y=gene, 
                fill = comparison_cohort)) +
  facet_wrap(~Sample_ID,
             nrow = 1) +
  theme(#axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
    axis.text.x = element_blank(),
    strip.text.x = element_text(angle = 90),
        ) +
  xlab("")  +
  scale_fill_bright()

Heatmap shows number of cohorts in which outlier were detected

I can make this look better if we decide to use it, but it’s non-trivial

outliers_heatmap_data <- outliers %>%
  group_by(Sample_ID, gene) %>%
  summarize(n_outliers = n()) 

## `summarise()` has grouped output by 'Sample_ID'. You can override using the
## `.groups` argument.

ggplot(outliers_heatmap_data) +
  geom_tile(aes(x=Sample_ID,
                y=gene,
                fill = n_outliers), 
            color = "black")  +
  #theme_bw() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) 

library(ggVennDiagram)
raw_outliers_for_venn <- outliers %>%
  mutate(sample_gene = paste(Sample_ID, gene, sep = "_")) %>%
  arrange(comparison_cohort) %>%
  select(sample_gene, comparison_cohort) %>%
  group_split(comparison_cohort)


list_of_outliers_for_venn <-  lapply(raw_outliers_for_venn, function(x) x %>% pull(sample_gene))
names(list_of_outliers_for_venn) <- unique(outliers$comparison_cohort) %>% sort

ggVennDiagram(list_of_outliers_for_venn,
              show_intersect = TRUE)

## Warning in geom_text(aes_string(label = "count", text = "text"), x =
## label_coord[, : Ignoring unknown aesthetics: text

ggVennDiagram(list_of_outliers_for_venn) + 
  scale_fill_distiller(palette = "Reds", direction = 1)

export list of genes found only by TCGA

# outliers %>%
#   group_by(Sample_ID, gene) %>%
#   filter(length(comparison_cohort) == 1,
#          "TCGA" %in% comparison_cohort) %>%
#   ungroup %>%
#   select(gene) %>% write_tsv("../gather_input_data/genes found only by TCGA in at least one sample.txt")

Annotate with combined full low level cohort names

collapse_fun <- function(x){ paste(x,collapse = ", ") }

all_outliers_combined_wide <- outliers %>%
  select(-pathway_support, -donor_ID, -high_level_cohort) %>%
  pivot_wider(names_from = Sample_ID,
              values_from = comparison_cohort,
              values_fn = collapse_fun)

n_distinct(outliers$Sample_ID)

## [1] 34

n_distinct(outliers$gene)

## [1] 56

all_outliers_combined_long <- all_outliers_combined_wide %>%
  pivot_longer(-gene,
               names_to = "Sample_ID",
               values_to = "comparison_cohorts") %>%
  na.omit()

How many outliers are present in each combination of cohorts?

tabyl(all_outliers_combined_long,
      comparison_cohorts) %>%
  arrange(desc(n)) %>%
  adorn_pct_formatting() %>%
  adorn_totals() %>%
  kbl() %>%
  kable_styling(full_width = F)

comparison_cohorts	n	percent
TCGA, Treehouse_pc	27	20.8%
TCGA	21	16.2%
Treehouse_pd	13	10.0%
TCGA, TH03_TH34, Treehouse_pc	12	9.2%
TH03_TH34	11	8.5%
PEDAYA, TCGA, TH03_TH34, Treehouse_pc	9	6.9%
TCGA, TH03_TH34, Treehouse_pc, Treehouse_pd	8	6.2%
PEDAYA, TCGA, TH03_TH34, Treehouse_pc, Treehouse_pd	7	5.4%
PEDAYA	5	3.8%
TCGA, Treehouse_pc, Treehouse_pd	4	3.1%
TCGA, TH03_TH34	3	2.3%
TCGA, Treehouse_pd	3	2.3%
PEDAYA, TCGA, Treehouse_pc, Treehouse_pd	2	1.5%
PEDAYA, TCGA, TH03_TH34	1	0.8%
PEDAYA, TCGA, Treehouse_pc	1	0.8%
PEDAYA, Treehouse_pc	1	0.8%
TH03_TH34, Treehouse_pc	1	0.8%
TH03_TH34, Treehouse_pd	1	0.8%
Total	130

Tile plot of combination of outliers

ggplot(all_outliers_combined_long) +
  geom_tile(aes(x=Sample_ID,
                y=gene,
                fill = comparison_cohorts))  +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

n_distinct(all_outliers_combined_long$Sample_ID)                

## [1] 34

Annotate with combined full high level cohort names

high_level_all_outliers_combined_wide <- outliers %>%
  select(-pathway_support, -donor_ID, -comparison_cohort) %>%
  distinct() %>%
  pivot_wider(names_from = Sample_ID,
              values_from = high_level_cohort,
              values_fn = collapse_fun)

n_distinct(outliers$Sample_ID)

## [1] 34

n_distinct(outliers$gene)

## [1] 56

high_level_all_outliers_combined_long <- high_level_all_outliers_combined_wide %>%
  pivot_longer(-gene,
               names_to = "Sample_ID",
               values_to = "comparison_cohorts") %>%
  na.omit()

How many outliers are present in each high level combination of cohorts?

tabyl(high_level_all_outliers_combined_long,
      comparison_cohorts) %>%
  arrange(desc(n)) %>%
  adorn_pct_formatting() %>%
  adorn_totals() %>%
  kbl() %>%
  kable_styling(full_width = F)

comparison_cohorts	n	percent
TCGA, Treehouse	34	26.2%
TCGA	21	16.2%
TCGA, TH03_TH34, Treehouse	20	15.4%
PEDAYA, TCGA, TH03_TH34, Treehouse	16	12.3%
Treehouse	13	10.0%
TH03_TH34	11	8.5%
PEDAYA	5	3.8%
PEDAYA, TCGA, Treehouse	3	2.3%
TCGA, TH03_TH34	3	2.3%
TH03_TH34, Treehouse	2	1.5%
PEDAYA, TCGA, TH03_TH34	1	0.8%
PEDAYA, Treehouse	1	0.8%
Total	130

Annotate with minimal combined cohort abbreviations

collapse_fun_no_coma <- function(x){ paste(x,collapse = "") }

# backslashes prevent asterisks from being interpreted as italics in the kbl table

all_outliers_min_abbrev_combined_wide <- outliers %>%
  left_join(cohort_codes,
            by=c("comparison_cohort"="cohort_name")) %>%
  mutate(cohort_code_pathway = ifelse(pathway_support,
                                      paste0(cohort_code, "\\*"),
                                      cohort_code)) %>%
  select(-pathway_support, -donor_ID,
         -comparison_cohort,
         -cohort_code) %>%
  pivot_wider(names_from = Sample_ID,
              values_from = cohort_code_pathway,
              values_fn = collapse_fun_no_coma,
              values_fill = "")


all_outliers_min_abbrev_combined_wide %>%
  arrange(gene) %>%
  rename_all(underscore_to_space) %>%
  kbl() %>%
  kable_styling(full_width = F,
                bootstrap_options = "bordered")

gene	high level cohort	TH34 1162 S01	TH34 1149 S02	TH34 1238 S01	TH34 1349 S01	TH34 1349 S02	TH34 1379 S01	TH34 1380 S01	TH34 1150 S02	TH34 1399 S01	TH34 1400 S01	TH34 1412 S01	TH34 1414 S01	TH34 1415 S01	TH34 1444 S01	TH34 1452 S01	TH34 2292 S01	TH34 2351 S01	TH34 2411 S01	TH34 2666 S01	TH34 1163 S01	TH34 1179 S01	TH34 1239 S01	TH34 1350 S01	TH34 1351 S01	TH34 1352 S01	TH34 1381 S01	TH34 1445 S02	TH34 1446 S01	TH34 1447 S01	TH34 1447 S02	TH34 1455 S01	TH34 1456 S02	TH34 2293 S01	TH34 2410 S01
AKT1	TH03_TH34														S*	S*
AKT1	Treehouse														D*
AKT2	PEDAYA														P*
AKT2	TCGA														T*
AKT2	TH03_TH34														S*
AKT2	Treehouse														C*D*
ALK	PEDAYA												P
ALK	TCGA												T
ALK	TH03_TH34												S
ALK	Treehouse												C
BCL6	TCGA																T*
BCL6	Treehouse								D
BTK	TCGA			T*																			T*	T*
BTK	TH03_TH34			S*
BTK	Treehouse			C*																			C*	C*
CCND1	TCGA								T*
CCND1	TH03_TH34								S*
CCND2	Treehouse						D*																			D
CCND3	TCGA			T*																			T*
CCNE1	Treehouse																													D*
CDK4	PEDAYA																		P*
CDK4	TCGA												T*						T*
CDK4	TH03_TH34												S*						S*
CDK4	Treehouse												C*						C*D*
CDK9	TCGA			T*																												T*
CDK9	TH03_TH34																															S
CDK9	Treehouse								D																							C
CSF1R	Treehouse	D*
DEPTOR	TH03_TH34														S*
ETV1	TCGA				T	T*											T											T
ETV1	Treehouse				C*	C*
FGFR1	TCGA																		T*			T*				T*
FGFR1	Treehouse																					C*				C*
FGFR2	TCGA																									T*
FGFR3	PEDAYA															P
FGFR3	TCGA															T*																T
FGFR3	TH03_TH34															S*
FGFR4	PEDAYA						P	P				P*	P					P
FGFR4	Treehouse												C
FLT4	PEDAYA																P*
FLT4	TCGA													T			T*			T
FLT4	TH03_TH34													S			S*
FLT4	Treehouse													CD			C*			C
GATA2	TCGA	T																															T*
GATA2	Treehouse																																CD*
HDAC4	PEDAYA															P
HDAC4	TCGA															T*
HDAC4	TH03_TH34															S*
HDAC4	Treehouse															CD*
HDAC7	Treehouse																																D*
HMOX1	PEDAYA	P												P*
HMOX1	TCGA	T					T*							T*			T*													T*	T*
HMOX1	Treehouse	CD*					C*D*							C*D			C*	D*										D*			C
HSP90B1	TCGA																										T*
HSP90B1	TH03_TH34																										S*
HSP90B1	Treehouse																										C*D*
IGF1	PEDAYA		P*							P*	P*
IGF1	TCGA		T*							T*
IGF1	TH03_TH34		S*
IGF1	Treehouse		C*D							C*
IGF2	TCGA				T*	T*	T*	T		T*		T*	T*			T*		T*			T					T*	T*	T*		T*	T	T		T*	T*
IGF2	Treehouse				C*		D*	C*		C*		C*	C			C*		C*			C					C*		D*			C	C		C*
IL6	PEDAYA													P*
IL6	TCGA													T*
IL6	TH03_TH34													S*
IL6	Treehouse													C*
JAK1	PEDAYA								P
JAK1	TCGA	T							T
JAK1	TH03_TH34	S							S
JAK1	Treehouse	C							C
KDR	TCGA											T*																					T*
KDR	TH03_TH34											S*
KDR	Treehouse											C*
KIT	PEDAYA				P	P									P*
KIT	TCGA				T	T									T*
KIT	TH03_TH34				S	S									S*
KIT	Treehouse				C	C									C*D*
MAP2K2	TCGA																		T*
MAP2K2	TH03_TH34							S*											S*
MAP2K2	Treehouse																		C*D*
MAP2K4	TCGA																								T
MAP2K4	TH03_TH34																								S
MAP2K4	Treehouse																								CD*
MDM2	PEDAYA																		P*	P
MDM2	TCGA																		T*	T
MDM2	TH03_TH34																		S*	S
MDM2	Treehouse																		C*D*	C
MS4A1	PEDAYA			P
MS4A1	TCGA			T*
MS4A1	TH03_TH34			S*
MS4A1	Treehouse			C*
MTOR	TCGA			T*
MTOR	TH03_TH34			S*
NOTCH3	TCGA							T*
NOTCH3	TH03_TH34							S*
NOTCH3	Treehouse							C*D*
NTRK2	TH03_TH34										S*				S*	S*											S*	S*	S*			S
NTRK2	Treehouse																															C
NTRK3	TCGA		T									T*																T	T
NTRK3	TH03_TH34																											S*
NTRK3	Treehouse		CD									C*																C	C
PARP1	Treehouse																										D
PARP2	TCGA																													T*	T*
PARP2	Treehouse																													C*D*
PDCD1	PEDAYA								P
PDCD1	TCGA								T
PDCD1	TH03_TH34								S
PDCD1	Treehouse								C
PDGFRA	TCGA																									T*
PIK3CD	TCGA			T*																			T*	T*
PIK3CD	TH03_TH34			S*																			S*
PIK3CD	Treehouse			C*																			C*
PIK3R1	TH03_TH34																										S*
PIK3R2	TCGA															T*
PIK3R2	TH03_TH34															S*
PIK3R2	Treehouse														D*	C*
PIK3R5	TCGA																						T*	T*
PIK3R5	Treehouse																						C*	C*
PTCH1	TCGA				T*	T*																T*
PTCH1	Treehouse					C*																C*
RAF1	Treehouse														D*
RPTOR	TCGA			T*
RPTOR	TH03_TH34			S*
STAT1	TH03_TH34																																	S
STAT2	TCGA												T
STAT2	TH03_TH34												S
STAT2	Treehouse												CD
STAT5A	Treehouse										D*
TSC2	TCGA														T*	T*											T*
TSC2	TH03_TH34														S*	S*
TSC2	Treehouse														C*D*	C*
VEGFA	TCGA																T*										T*	T*
VEGFA	TH03_TH34																S*										S*
VEGFA	Treehouse																C*										C*	D*
VEGFC	PEDAYA														P*
VEGFC	TCGA														T*
VEGFC	TH03_TH34														S*
VEGFC	Treehouse														C*D*
WEE1	TCGA																											T*
WEE1	TH03_TH34																											S
WEE1	Treehouse																											C*D*

Annotate with combined cohort abbreviations

all_outliers_abbrev_combined_wide <- outliers %>%
  left_join(cohort_codes,
            by=c("comparison_cohort"="cohort_name")) %>%
  select(-pathway_support, -donor_ID,
         -comparison_cohort) %>%
  pivot_wider(names_from = Sample_ID,
              values_from = cohort_code,
              values_fn = collapse_fun,
              values_fill = "")

all_outliers_abbrev_combined_wide %>%
  arrange(gene) %>%
  rename_all(underscore_to_space) %>%
  kbl() %>%
  kable_styling(full_width = F,
                bootstrap_options = "bordered")

gene	high level cohort	TH34 1162 S01	TH34 1149 S02	TH34 1238 S01	TH34 1349 S01	TH34 1349 S02	TH34 1379 S01	TH34 1380 S01	TH34 1150 S02	TH34 1399 S01	TH34 1400 S01	TH34 1412 S01	TH34 1414 S01	TH34 1415 S01	TH34 1444 S01	TH34 1452 S01	TH34 2292 S01	TH34 2351 S01	TH34 2411 S01	TH34 2666 S01	TH34 1163 S01	TH34 1179 S01	TH34 1239 S01	TH34 1350 S01	TH34 1351 S01	TH34 1352 S01	TH34 1381 S01	TH34 1445 S02	TH34 1446 S01	TH34 1447 S01	TH34 1447 S02	TH34 1455 S01	TH34 1456 S02	TH34 2293 S01	TH34 2410 S01
AKT1	TH03_TH34														S	S
AKT1	Treehouse														D
AKT2	PEDAYA														P
AKT2	TCGA														T
AKT2	TH03_TH34														S
AKT2	Treehouse														C, D
ALK	PEDAYA												P
ALK	TCGA												T
ALK	TH03_TH34												S
ALK	Treehouse												C
BCL6	TCGA																T
BCL6	Treehouse								D
BTK	TCGA			T																			T	T
BTK	TH03_TH34			S
BTK	Treehouse			C																			C	C
CCND1	TCGA								T
CCND1	TH03_TH34								S
CCND2	Treehouse						D																			D
CCND3	TCGA			T																			T
CCNE1	Treehouse																													D
CDK4	PEDAYA																		P
CDK4	TCGA												T						T
CDK4	TH03_TH34												S						S
CDK4	Treehouse												C						C, D
CDK9	TCGA			T																												T
CDK9	TH03_TH34																															S
CDK9	Treehouse								D																							C
CSF1R	Treehouse	D
DEPTOR	TH03_TH34														S
ETV1	TCGA				T	T											T											T
ETV1	Treehouse				C	C
FGFR1	TCGA																		T			T				T
FGFR1	Treehouse																					C				C
FGFR2	TCGA																									T
FGFR3	PEDAYA															P
FGFR3	TCGA															T																T
FGFR3	TH03_TH34															S
FGFR4	PEDAYA						P	P				P	P					P
FGFR4	Treehouse												C
FLT4	PEDAYA																P
FLT4	TCGA													T			T			T
FLT4	TH03_TH34													S			S
FLT4	Treehouse													C, D			C			C
GATA2	TCGA	T																															T
GATA2	Treehouse																																C, D
HDAC4	PEDAYA															P
HDAC4	TCGA															T
HDAC4	TH03_TH34															S
HDAC4	Treehouse															C, D
HDAC7	Treehouse																																D
HMOX1	PEDAYA	P												P
HMOX1	TCGA	T					T							T			T													T	T
HMOX1	Treehouse	C, D					C, D							C, D			C	D										D			C
HSP90B1	TCGA																										T
HSP90B1	TH03_TH34																										S
HSP90B1	Treehouse																										C, D
IGF1	PEDAYA		P							P	P
IGF1	TCGA		T							T
IGF1	TH03_TH34		S
IGF1	Treehouse		C, D							C
IGF2	TCGA				T	T	T	T		T		T	T			T		T			T					T	T	T		T	T	T		T	T
IGF2	Treehouse				C		D	C		C		C	C			C		C			C					C		D			C	C		C
IL6	PEDAYA													P
IL6	TCGA													T
IL6	TH03_TH34													S
IL6	Treehouse													C
JAK1	PEDAYA								P
JAK1	TCGA	T							T
JAK1	TH03_TH34	S							S
JAK1	Treehouse	C							C
KDR	TCGA											T																					T
KDR	TH03_TH34											S
KDR	Treehouse											C
KIT	PEDAYA				P	P									P
KIT	TCGA				T	T									T
KIT	TH03_TH34				S	S									S
KIT	Treehouse				C	C									C, D
MAP2K2	TCGA																		T
MAP2K2	TH03_TH34							S											S
MAP2K2	Treehouse																		C, D
MAP2K4	TCGA																								T
MAP2K4	TH03_TH34																								S
MAP2K4	Treehouse																								C, D
MDM2	PEDAYA																		P	P
MDM2	TCGA																		T	T
MDM2	TH03_TH34																		S	S
MDM2	Treehouse																		C, D	C
MS4A1	PEDAYA			P
MS4A1	TCGA			T
MS4A1	TH03_TH34			S
MS4A1	Treehouse			C
MTOR	TCGA			T
MTOR	TH03_TH34			S
NOTCH3	TCGA							T
NOTCH3	TH03_TH34							S
NOTCH3	Treehouse							C, D
NTRK2	TH03_TH34										S				S	S											S	S	S			S
NTRK2	Treehouse																															C
NTRK3	TCGA		T									T																T	T
NTRK3	TH03_TH34																											S
NTRK3	Treehouse		C, D									C																C	C
PARP1	Treehouse																										D
PARP2	TCGA																													T	T
PARP2	Treehouse																													C, D
PDCD1	PEDAYA								P
PDCD1	TCGA								T
PDCD1	TH03_TH34								S
PDCD1	Treehouse								C
PDGFRA	TCGA																									T
PIK3CD	TCGA			T																			T	T
PIK3CD	TH03_TH34			S																			S
PIK3CD	Treehouse			C																			C
PIK3R1	TH03_TH34																										S
PIK3R2	TCGA															T
PIK3R2	TH03_TH34															S
PIK3R2	Treehouse														D	C
PIK3R5	TCGA																						T	T
PIK3R5	Treehouse																						C	C
PTCH1	TCGA				T	T																T
PTCH1	Treehouse					C																C
RAF1	Treehouse														D
RPTOR	TCGA			T
RPTOR	TH03_TH34			S
STAT1	TH03_TH34																																	S
STAT2	TCGA												T
STAT2	TH03_TH34												S
STAT2	Treehouse												C, D
STAT5A	Treehouse										D
TSC2	TCGA														T	T											T
TSC2	TH03_TH34														S	S
TSC2	Treehouse														C, D	C
VEGFA	TCGA																T										T	T
VEGFA	TH03_TH34																S										S
VEGFA	Treehouse																C										C	D
VEGFC	PEDAYA														P
VEGFC	TCGA														T
VEGFC	TH03_TH34														S
VEGFC	Treehouse														C, D
WEE1	TCGA																											T
WEE1	TH03_TH34																											S
WEE1	Treehouse																											C, D

SUMMARY TABLE FOR OUTLIERS DETECTED BY TREEHOUSE and OTHER COHORTS (excludes pan-disease)

treehouse_pc_outliers <- outliers %>%
  filter(comparison_cohort == "Treehouse_pc") %>%
  select(Sample_ID, gene) %>%
  distinct()

outliers_detected_by_treehouse_pc <- left_join(treehouse_pc_outliers, 
                                            outliers %>%
                                              filter(comparison_cohort != "Treehouse_pd"), 
                                            by = c("Sample_ID", "gene"))

n_outliers_detected_by_treehouse_pc <- treehouse_pc_outliers %>%
  nrow()

n_outliers_with_pathway_support_detected_by_treehouse_pc <- outliers_detected_by_treehouse_pc %>%
  filter(pathway_support) %>%
  select(Sample_ID, gene) %>%
  distinct %>%
  nrow()
# these have pathway support in at least one cohort

treehouse_pc_totals_tibble <-  tibble(high_level_cohort= " Total",
                         comparison_cohort = " Total",
                 n_outliers_detected = n_outliers_detected_by_treehouse_pc,
                 n_outliers_with_pathway_support = n_outliers_with_pathway_support_detected_by_treehouse_pc,
                 pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support_detected_by_treehouse_pc/n_outliers_detected_by_treehouse_pc)

treehouse_pc_outlier_summary <- outliers_detected_by_treehouse_pc %>% 
  group_by(comparison_cohort) %>%
  summarize(n_outliers_detected = n(),
         n_outliers_with_pathway_support = sum(pathway_support)) %>%
  ungroup() %>%
  mutate(pct_outliers_detected = 100*n_outliers_detected/n_outliers_detected_by_treehouse_pc,
         pct_outliers_with_pathway_support_detected =
           100*n_outliers_with_pathway_support/n_outliers_with_pathway_support_detected_by_treehouse_pc,
         pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support/n_outliers_detected)
  
treehouse_pc_outlier_summary_with_totals <- 
  bind_rows(treehouse_pc_outlier_summary,
            treehouse_pc_totals_tibble %>% select(-high_level_cohort))

                 
  
treehouse_pc_outlier_summary_with_totals %>% 
  rename_all(underscore_to_space) %>%
  kbl(digits = c(NA, 0, 0, 0, 0)) %>%
  kable_styling(full_width = F)

comparison cohort	n outliers detected	n outliers with pathway support	pct outliers detected	pct outliers with pathway support detected	pct outliers with pathway support
PEDAYA	20	10	28	19	NA
TCGA	70	50	97	94	NA
TH03_TH34	37	24	51	45	NA
Treehouse_pc	72	47	100	89	NA
Total	72	53	NA	NA	NA

SUMMARY TABLE FOR OUTLIERS DETECTED BY TREEHOUSE and OTHER COHORTS

treehouse_outliers <- outliers %>%
  filter(str_detect(high_level_cohort, "Treehouse")) %>%
  select(Sample_ID, gene) %>%
  distinct()

outliers_detected_by_treehouse <- left_join(treehouse_outliers, 
                                            outliers, 
                                            by = c("Sample_ID", "gene"))

n_outliers_detected_by_treehouse <- treehouse_outliers %>%
  nrow()

n_outliers_with_pathway_support_detected_by_treehouse <- outliers_detected_by_treehouse %>%
  filter(pathway_support) %>%
  select(Sample_ID, gene) %>%
  distinct %>%
  nrow()
# these have pathway support in at least one cohort

treehouse_totals_tibble <-  tibble(high_level_cohort= " Total",
                         comparison_cohort = " Total",
                 n_outliers_detected = n_outliers_detected_by_treehouse,
                 n_outliers_with_pathway_support = n_outliers_with_pathway_support_detected_by_treehouse,
                 pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support_detected_by_treehouse/n_outliers_detected_by_treehouse)

treehouse_outlier_summary <- outliers_detected_by_treehouse %>% 
  group_by(comparison_cohort) %>%
  summarize(n_outliers_detected = n(),
         n_outliers_with_pathway_support = sum(pathway_support)) %>%
  ungroup() %>%
  mutate(pct_outliers_detected = 100*n_outliers_detected/n_outliers_detected_by_treehouse,
         pct_outliers_with_pathway_support_detected =
           100*n_outliers_with_pathway_support/n_outliers_with_pathway_support_detected_by_treehouse,
         pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support/n_outliers_detected)
  
treehouse_outlier_summary_with_totals <- 
  bind_rows(treehouse_outlier_summary,
            treehouse_totals_tibble %>% select(-high_level_cohort))

                 
  
treehouse_outlier_summary_with_totals %>% 
  rename_all(underscore_to_space) %>%
  kbl(digits = c(NA, 0, 0, 0, 0)) %>%
  kable_styling(full_width = F)

comparison cohort	n outliers detected	n outliers with pathway support	pct outliers detected	pct outliers with pathway support detected	pct outliers with pathway support
PEDAYA	20	10	22	15	NA
TCGA	73	53	82	78	NA
TH03_TH34	38	25	43	37	NA
Treehouse_pc	72	47	81	69	NA
Treehouse_pd	38	29	43	43	NA
Total	89	68	NA	NA	NA

Summary table for treehouse outliers detected by high level cohorts

high_level_treehouse_outlier_summary <- outliers_detected_by_treehouse %>% 
  group_by(high_level_cohort, Sample_ID, gene) %>%
  summarize(pathway_support = any(pathway_support)) %>%
  group_by(high_level_cohort) %>%
  summarize(n_outliers_detected = n(),
         n_outliers_with_pathway_support = sum(pathway_support),
         pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support/n_outliers_detected,
         pct_outliers_detected = 100*n_outliers_detected/n_outliers_detected_by_treehouse)

## `summarise()` has grouped output by 'high_level_cohort', 'Sample_ID'. You can
## override using the `.groups` argument.

Function for renaming cohorts

update_cohort_strings <- function(x) {
  str_replace(x,
              "PEDAYA", 
              "Pediatric") %>%
    str_replace("TH03_TH34",
                "Stanford")%>%
    str_replace("Treehouse_pc",
                "Treehouse pan-cancer")%>%
    str_replace("Treehouse_pd",
                "Treehouse pan-disease") %>%
    str_replace_all("_([a-z12][a-z])", " \\1")
  
}

Combined high and low level tables for treehouse outliers

treehouse_high_low <- bind_rows(
  high_level_treehouse_outlier_summary %>% 
    rename(comparison_cohort=high_level_cohort) %>%
    mutate(index = c(6, 5, 4, 1)),
  treehouse_outlier_summary %>%
    filter(str_detect(comparison_cohort, "Treehouse")) %>%
    mutate(index = 2:3)
  # ,
  # treehouse_totals_tibble %>% 
  #   select(-high_level_cohort)  %>%
  #   mutate(index = 7)
) %>% 
  arrange(index) %>%
  select(-index) 

treehouse_high_low_outlier_summary <- treehouse_high_low %>%
  mutate(
    pct_outliers_with_pathway_support_detected =
      100*n_outliers_with_pathway_support/n_outliers_with_pathway_support_detected_by_treehouse,
    `Druggable outliers detected` = 
      paste0(n_outliers_detected, "/", n_outliers_detected_by_treehouse, " (",
             round(pct_outliers_detected), "%)"),
    `Druggable outliers with pathway support` = 
      paste0(n_outliers_with_pathway_support, "/",
             n_outliers_with_pathway_support_detected_by_treehouse, " (",
             round(pct_outliers_with_pathway_support_detected), "%)"),
    `Fraction of druggable outliers with pathway support` = 
      paste0(n_outliers_with_pathway_support, "/", 
             n_outliers_detected, " (",
             round(100*n_outliers_with_pathway_support/n_outliers_detected), "%)")
  ) %>%
  select(comparison_cohort, 
         `Druggable outliers detected`,
         `Druggable outliers with pathway support`,
         `Fraction of druggable outliers with pathway support`) %>%
  bind_rows(treehouse_totals_tibble %>%
              mutate(`Fraction of druggable outliers with pathway support` =
                       paste0(n_outliers_with_pathway_support, "/", 
                              n_outliers_detected, " (",
                              round(100*n_outliers_with_pathway_support/n_outliers_detected), "%)"),
                     `Druggable outliers detected` = as.character(n_outliers_detected),
                     `Druggable outliers with pathway support` = as.character(n_outliers_with_pathway_support)
              ) %>%
              select(comparison_cohort, 
                     `Druggable outliers detected`,
                     `Druggable outliers with pathway support`,
                     `Fraction of druggable outliers with pathway support`)
  ) %>%
  mutate(comparison_cohort = update_cohort_strings(comparison_cohort))



treehouse_high_low_outlier_summary %>%
  rename_all(underscore_to_space_initial_cap) %>%
  kbl() %>%
  kable_styling(full_width = F) %>%
  add_indent(c(2, 3), level_of_indent = 1)

Comparison cohort	Druggable outliers detected	Druggable outliers with pathway support	Fraction of druggable outliers with pathway support
Treehouse	89/89 (100%)	64/68 (94%)	64/89 (72%)
Treehouse pan-cancer	72/89 (81%)	47/68 (69%)	47/72 (65%)
Treehouse pan-disease	38/89 (43%)	29/68 (43%)	29/38 (76%)
Stanford	38/89 (43%)	25/68 (37%)	25/38 (66%)
TCGA	73/89 (82%)	53/68 (78%)	53/73 (73%)
Pediatric	20/89 (22%)	10/68 (15%)	10/20 (50%)
Total	89	68	68/89 (76%)

table_output_name <- paste0("../results/treehouse_outliers_detected_relative_to_different_cohorts_", format(Sys.time(), "%Y_%m_%d-%H_%M_%S_%Y"), ".tsv")

write_tsv(treehouse_high_low_outlier_summary %>%
  rename_all(underscore_to_space_initial_cap), table_output_name)

this output was saved as ../results/treehouse_outliers_detected_relative_to_different_cohorts_2024_02_16-16_12_39_2024.tsv

SUMMARY TABLE FOR OUTLIERS DETECTED BY any COHORT, INCLUDES AND LOW LEVEL COHORTS

n_outliers_detected_by_any_method <- outliers %>%
  select(Sample_ID, gene) %>%
  distinct %>%
  nrow()

n_outliers_with_pathway_support_detected_by_any_method <- outliers %>%
  filter(pathway_support) %>%
  select(Sample_ID, gene) %>%
  distinct %>%
  nrow()
# these have pathway support in at least one cohort

totals_tibble <-  tibble(high_level_cohort= " Total",
                         comparison_cohort = " Total",
                 n_outliers_detected = n_outliers_detected_by_any_method,
                 n_outliers_with_pathway_support = n_outliers_with_pathway_support_detected_by_any_method,
                 pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support_detected_by_any_method/n_outliers_detected_by_any_method)

outlier_summary <- outliers %>% 
  group_by(comparison_cohort) %>%
  summarize(n_outliers_detected = n(),
         n_outliers_with_pathway_support = sum(pathway_support)) %>%
  ungroup() %>%
  mutate(pct_outliers_detected = 100*n_outliers_detected/n_outliers_detected_by_any_method,
         pct_outliers_with_pathway_support_detected =
           100*n_outliers_with_pathway_support/n_outliers_with_pathway_support_detected_by_any_method,
         pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support/n_outliers_detected)
  
outlier_summary_with_totals <- 
  bind_rows(outlier_summary,
            totals_tibble %>% select(-high_level_cohort))

outlier_summary_with_totals %>% 
  rename_all(underscore_to_space) %>%
  kbl(digits = c(NA, 0, 0, 0, 0)) %>%
  kable_styling(full_width = F)

comparison cohort	n outliers detected	n outliers with pathway support	pct outliers detected	pct outliers with pathway support detected	pct outliers with pathway support
PEDAYA	26	12	20	12	NA
TCGA	98	74	75	73	NA
TH03_TH34	53	39	41	39	NA
Treehouse_pc	72	47	55	47	NA
Treehouse_pd	38	29	29	29	NA
Total	130	101	NA	NA	NA

Summary table for all outliers and high level cohorts

high_level_outlier_summary <- outliers %>% 
  group_by(high_level_cohort, Sample_ID, gene) %>%
  summarize(pathway_support = any(pathway_support)) %>%
  group_by(high_level_cohort) %>%
  summarize(n_outliers_detected = n(),
         n_outliers_with_pathway_support = sum(pathway_support),
         pct_outliers_with_pathway_support = 100*n_outliers_with_pathway_support/n_outliers_detected,
         pct_outliers_detected = 100*n_outliers_detected/n_outliers_detected_by_any_method)

## `summarise()` has grouped output by 'high_level_cohort', 'Sample_ID'. You can
## override using the `.groups` argument.

high_level_outlier_summary_with_totals <- 
  bind_rows(high_level_outlier_summary %>%
              arrange(desc(high_level_cohort)),
            totals_tibble %>% select(-comparison_cohort))



high_level_outlier_summary_with_totals %>% 
  rename_all(underscore_to_space) %>%
  kbl(format.args = list(big.mark = ","), digits = c(NA, 0, 0, 0, 0)) %>%
  kable_styling(full_width = F)

high level cohort	n outliers detected	n outliers with pathway support	pct outliers with pathway support	pct outliers detected
Treehouse	89	64	72	68
TH03_TH34	53	39	74	41
TCGA	98	74	76	75
PEDAYA	26	12	46	20
Total	130	101	78	NA

Combined high and low level tables

high_low <- bind_rows(
  high_level_outlier_summary_with_totals %>% 
    rename(comparison_cohort=high_level_cohort) %>%
    mutate(index = c(1, 4:7)),
  outlier_summary_with_totals %>%
    filter(str_detect(comparison_cohort, "Treehouse")) %>%
    mutate(index = 2:3)
) %>% 
  arrange(index) %>%
  select(-index) 
       

high_low_outlier_summary <- high_low %>%
  filter(! str_detect(comparison_cohort, "Total")) %>%
  mutate(
    comparison_cohort = str_replace(comparison_cohort, "_", " "),
    pct_outliers_with_pathway_support_detected =
      100*n_outliers_with_pathway_support/n_outliers_with_pathway_support_detected_by_any_method,
    `Druggable outliers detected` = 
      paste0(n_outliers_detected, "/", n_outliers_detected_by_any_method, " (",
             round(pct_outliers_detected), "%)"),
    `Druggable outliers with pathway support` = 
      paste0(n_outliers_with_pathway_support, "/",
             n_outliers_with_pathway_support_detected_by_any_method, " (",
             round(pct_outliers_with_pathway_support_detected), "%)"),
    `Fraction of druggable outliers with pathway support` = 
      paste0(n_outliers_with_pathway_support, "/", 
             n_outliers_detected, " (",
             round(100*n_outliers_with_pathway_support/n_outliers_detected), "%)")
  ) %>%
  select(comparison_cohort, 
         `Druggable outliers detected`,
         `Druggable outliers with pathway support`,
         `Fraction of druggable outliers with pathway support`) %>%
  bind_rows(totals_tibble %>%
              mutate(`Fraction of druggable outliers with pathway support` =
                       paste0(n_outliers_with_pathway_support, "/", 
                              n_outliers_detected, " (",
                              round(100*n_outliers_with_pathway_support/n_outliers_detected), "%)"),
                     `Druggable outliers detected` = as.character(n_outliers_detected),
                     `Druggable outliers with pathway support` = as.character(n_outliers_with_pathway_support)
              ) %>%
              select(comparison_cohort, 
                     `Druggable outliers detected`,
                     `Druggable outliers with pathway support`,
                     `Fraction of druggable outliers with pathway support`)
  )

         


  

high_low_outlier_summary %>%
  rename_all(underscore_to_space_initial_cap) %>%
  kbl() %>%
  kable_styling(full_width = F) %>%
  add_indent(c(2, 3), level_of_indent = 1)

Comparison cohort	Druggable outliers detected	Druggable outliers with pathway support	Fraction of druggable outliers with pathway support
Treehouse	89/130 (68%)	64/101 (63%)	64/89 (72%)
Treehouse pc	72/130 (55%)	47/101 (47%)	47/72 (65%)
Treehouse pd	38/130 (29%)	29/101 (29%)	29/38 (76%)
TH03 TH34	53/130 (41%)	39/101 (39%)	39/53 (74%)
TCGA	98/130 (75%)	74/101 (73%)	74/98 (76%)
PEDAYA	26/130 (20%)	12/101 (12%)	12/26 (46%)
Total	130	101	101/130 (78%)

Patient level summary table for all outliers

outliers %>%
  group_by(donor_ID) %>%
  summarize(any_PEDAYA = "PEDAYA" %in% comparison_cohort,
            any_TH03_TH34 = "TH03_TH34" %in% comparison_cohort,
            any_TCGA = "TCGA" %in% comparison_cohort,
            any_Treehouse_pc = "Treehouse_pc" %in% comparison_cohort,
            any_Treehouse_pd = "Treehouse_pd" %in% comparison_cohort,
            any_Treehouse = any_Treehouse_pc | any_Treehouse_pd) %>%
  pivot_longer(starts_with("any")) %>%
  mutate(name = str_remove(name, "any_")) %>%
  group_by(name) %>%
  summarize(n_patients_with_druggable_outliers = sum(value)) %>% 
  kbl() %>%
  kable_styling(full_width = F)

name	n_patients_with_druggable_outliers
PEDAYA	18
TCGA	31
TH03_TH34	22
Treehouse	31
Treehouse_pc	30
Treehouse_pd	18

Sample level summary table for all outliers

outliers %>%
  group_by(Sample_ID) %>%
  summarize(any_PEDAYA = "PEDAYA" %in% comparison_cohort,
            any_TH03_TH34 = "TH03_TH34" %in% comparison_cohort,
            any_TCGA = "TCGA" %in% comparison_cohort,
            any_Treehouse_pc = "Treehouse_pc" %in% comparison_cohort,
            any_Treehouse_pd = "Treehouse_pd" %in% comparison_cohort,
            any_Treehouse = any_Treehouse_pc | any_Treehouse_pd) %>%
  pivot_longer(starts_with("any")) %>%
  mutate(name = str_remove(name, "any_")) %>%
  group_by(name) %>%
  summarize(n_samples_with_druggable_outliers = sum(value)) %>% 
  kbl() %>%
  kable_styling(full_width = F)

name	n_samples_with_druggable_outliers
PEDAYA	19
TCGA	33
TH03_TH34	23
Treehouse	33
Treehouse_pc	32
Treehouse_pd	18

” The genes represent 56 of the 115 druggable genes we consider (eTable)”

all_druggable_genes <- read_tsv("../input_data/treehouseDruggableGenes_2020-03_25.txt")

## Rows: 115 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): gene, group
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

all_druggable_genes %>%
 mutate( found_as_treehouse_outlier = gene %in% (outliers %>%
  filter(high_level_cohort == "Treehouse") %>%
    pull(gene))) %>%
  tabyl(found_as_treehouse_outlier)

##  found_as_treehouse_outlier  n percent
##                       FALSE 69     0.6
##                        TRUE 46     0.4

REPEAT ANALYSIS USING ONLY OUTLIERS WITH PATHWAY SUPPORT

Tile plot of outliers with pathway support

ggplot(outliers %>%
         filter(pathway_support)) +
  geom_tile(aes(x=comparison_cohort,
                y=gene, 
                fill = comparison_cohort)) +
  facet_wrap(~Sample_ID,
             nrow = 1) +
  theme(#axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
    axis.text.x = element_blank(),
    strip.text.x = element_text(angle = 90),
        ) +
  xlab("")  +
  scale_fill_bright()

Heatmap shows number of cohorts in which outlier were detected

I can make this look better if we decide to use it, but it’s non-trivial

pathway_outliers_heatmap_data <- outliers %>%
  filter(pathway_support) %>%
  group_by(Sample_ID, gene) %>%
  summarize(n_outliers = n()) 

## `summarise()` has grouped output by 'Sample_ID'. You can override using the
## `.groups` argument.

ggplot(pathway_outliers_heatmap_data) +
  geom_tile(aes(x=Sample_ID,
                y=gene,
                fill = n_outliers), 
            color = "black")  +
  #theme_bw() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) 

raw_pathway_support_outliers_for_venn <- outliers %>%
  filter(pathway_support)  %>%
  mutate(sample_gene = paste(Sample_ID, gene, sep = "_")) %>%
  arrange(comparison_cohort) %>%
  select(sample_gene, comparison_cohort) %>%
  group_split(comparison_cohort)


list_of_pathway_support_outliers_for_venn <-  lapply(raw_pathway_support_outliers_for_venn, function(x) x %>% pull(sample_gene))
names(list_of_pathway_support_outliers_for_venn) <- outliers %>%
  filter(pathway_support) %>%
  arrange(comparison_cohort) %>%
  select(comparison_cohort) %>%
  distinct() %>%
  pull(comparison_cohort)

ggVennDiagram(list_of_pathway_support_outliers_for_venn,
              show_intersect = TRUE)

## Warning in geom_text(aes_string(label = "count", text = "text"), x =
## label_coord[, : Ignoring unknown aesthetics: text

ggVennDiagram(list_of_pathway_support_outliers_for_venn) + 
  scale_fill_distiller(palette = "Reds", direction = 1)

Annotate with combined full cohort names

outliers_with_pathway_support_combined_wide <- outliers %>%
  filter(pathway_support) %>%
  select(-pathway_support, -donor_ID) %>%
  pivot_wider(names_from = Sample_ID,
              values_from = comparison_cohort,
              values_fn = collapse_fun)

outliers_with_pathway_support_combined_long <- outliers_with_pathway_support_combined_wide %>%
  pivot_longer(c(-gene, -high_level_cohort),
               names_to = "Sample_ID",
               values_to = "comparison_cohorts") %>%
  na.omit()

How many outliers with pathway support are present in each combination of cohorts?

tabyl(outliers_with_pathway_support_combined_long,
      comparison_cohorts) %>%
  arrange(desc(n)) %>%
  adorn_pct_formatting() %>%
  adorn_totals() %>%
  kbl() %>%
  kable_styling(full_width = F)

comparison_cohorts	n	percent
TCGA	74	39.2%
TH03_TH34	39	20.6%
Treehouse_pc	35	18.5%
Treehouse_pd	17	9.0%
PEDAYA	12	6.3%
Treehouse_pc, Treehouse_pd	12	6.3%
Total	189

ggplot(outliers_with_pathway_support_combined_long) +
  geom_tile(aes(x=Sample_ID,
                y=gene,
                fill = comparison_cohorts))  +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

n_distinct(outliers_with_pathway_support_combined_long$Sample_ID)                

## [1] 32

Patient level summary table for outliers with pathway support

outliers %>%
  filter(pathway_support) %>%
  group_by(donor_ID) %>%
  summarize(any_PEDAYA = "PEDAYA" %in% comparison_cohort,
            any_TH03_TH34 = "TH03_TH34" %in% comparison_cohort,
            any_TCGA = "TCGA" %in% comparison_cohort,
            any_Treehouse_pc = "Treehouse_pc" %in% comparison_cohort,
            any_Treehouse_pd = "Treehouse_pd" %in% comparison_cohort,
            any_Treehouse = any_Treehouse_pc | any_Treehouse_pd) %>%
  pivot_longer(starts_with("any")) %>%
  mutate(name = str_remove(name, "any_")) %>%
  group_by(name) %>%
  summarize(n_patients_with_druggable_outliers = sum(value)) %>% 
  kbl() %>%
  kable_styling(full_width = F)

name	n_patients_with_druggable_outliers
PEDAYA	8
TCGA	26
TH03_TH34	16
Treehouse	26
Treehouse_pc	22
Treehouse_pd	13

Annotate with combined cohort abbreviations

outliers_with_pathway_support_abbrev_combined_wide <- outliers %>%
  filter(pathway_support) %>%
  left_join(cohort_codes,
            by=c("comparison_cohort"="cohort_name")) %>%
  select(-pathway_support, -donor_ID,
         -comparison_cohort) %>%
  pivot_wider(names_from = Sample_ID,
              values_from = cohort_code,
              values_fn = collapse_fun,
              values_fill = "")

Big table of outliers with pathway support

outliers_with_pathway_support_abbrev_combined_wide %>%
  arrange(gene) %>%
  rename_all(underscore_to_space) %>%
  kbl() %>%
  kable_styling(full_width = F,
                bootstrap_options = "bordered")

gene	high level cohort	TH34 1149 S02	TH34 1238 S01	TH34 1399 S01	TH34 1400 S01	TH34 1412 S01	TH34 1415 S01	TH34 1444 S01	TH34 1452 S01	TH34 2292 S01	TH34 2411 S01	TH34 1179 S01	TH34 1239 S01	TH34 1349 S01	TH34 1349 S02	TH34 1350 S01	TH34 1352 S01	TH34 1379 S01	TH34 1380 S01	TH34 1381 S01	TH34 1150 S02	TH34 1414 S01	TH34 1445 S02	TH34 1447 S01	TH34 1447 S02	TH34 1455 S01	TH34 1456 S02	TH34 2293 S01	TH34 2351 S01	TH34 2410 S01	TH34 1446 S01	TH34 1162 S01	TH34 1351 S01
AKT1	TH03_TH34							S	S
AKT1	Treehouse							D
AKT2	PEDAYA							P
AKT2	TCGA							T
AKT2	TH03_TH34							S
AKT2	Treehouse							C, D
BCL6	TCGA									T
BTK	TCGA		T										T			T
BTK	TH03_TH34		S
BTK	Treehouse		C										C			C
CCND1	TCGA																				T
CCND1	TH03_TH34																				S
CCND2	Treehouse																	D
CCND3	TCGA		T										T
CCNE1	Treehouse																							D
CDK4	PEDAYA										P
CDK4	TCGA										T											T
CDK4	TH03_TH34										S											S
CDK4	Treehouse										C, D											C
CDK9	TCGA		T																							T
CSF1R	Treehouse																															D
DEPTOR	TH03_TH34							S
ETV1	TCGA														T
ETV1	Treehouse													C	C
FGFR1	TCGA										T	T					T
FGFR1	Treehouse											C					C
FGFR2	TCGA																T
FGFR3	TCGA								T
FGFR3	TH03_TH34								S
FGFR4	PEDAYA					P
FLT4	PEDAYA									P
FLT4	TCGA									T
FLT4	TH03_TH34									S
FLT4	Treehouse									C
GATA2	TCGA																										T
GATA2	Treehouse																										D
HDAC4	TCGA								T
HDAC4	TH03_TH34								S
HDAC4	Treehouse								D
HDAC7	Treehouse																										D
HMOX1	PEDAYA						P
HMOX1	TCGA						T			T								T						T	T
HMOX1	Treehouse						C			C								C, D					D						D			D
HSP90B1	TCGA																			T
HSP90B1	TH03_TH34																			S
HSP90B1	Treehouse																			C, D
IGF1	PEDAYA	P		P	P
IGF1	TCGA	T		T
IGF1	TH03_TH34	S
IGF1	Treehouse	C		C
IGF2	TCGA			T		T			T					T	T		T	T		T		T	T	T				T	T	T
IGF2	Treehouse			C		C			C					C			C	D	C				D					C	C
IL6	PEDAYA						P
IL6	TCGA						T
IL6	TH03_TH34						S
IL6	Treehouse						C
KDR	TCGA					T																					T
KDR	TH03_TH34					S
KDR	Treehouse					C
KIT	PEDAYA							P
KIT	TCGA							T
KIT	TH03_TH34							S
KIT	Treehouse							C, D
MAP2K2	TCGA										T
MAP2K2	TH03_TH34										S								S
MAP2K2	Treehouse										C, D
MAP2K4	Treehouse																																D
MDM2	PEDAYA										P
MDM2	TCGA										T
MDM2	TH03_TH34										S
MDM2	Treehouse										C, D
MS4A1	TCGA		T
MS4A1	TH03_TH34		S
MS4A1	Treehouse		C
MTOR	TCGA		T
MTOR	TH03_TH34		S
NOTCH3	TCGA																		T
NOTCH3	TH03_TH34																		S
NOTCH3	Treehouse																		C, D
NTRK2	TH03_TH34				S			S	S											S			S								S
NTRK3	TCGA					T
NTRK3	TH03_TH34																						S
NTRK3	Treehouse					C
PARP2	TCGA																							T	T
PARP2	Treehouse																							C, D
PDGFRA	TCGA																T
PIK3CD	TCGA		T										T			T
PIK3CD	TH03_TH34		S										S
PIK3CD	Treehouse		C										C
PIK3R1	TH03_TH34																			S
PIK3R2	TCGA								T
PIK3R2	TH03_TH34								S
PIK3R2	Treehouse							D	C
PIK3R5	TCGA												T			T
PIK3R5	Treehouse												C			C
PTCH1	TCGA											T		T	T
PTCH1	Treehouse											C			C
RAF1	Treehouse							D
RPTOR	TCGA		T
RPTOR	TH03_TH34		S
STAT5A	Treehouse				D
TSC2	TCGA							T	T											T
TSC2	TH03_TH34							S	S
TSC2	Treehouse							C, D	C
VEGFA	TCGA									T										T			T
VEGFA	TH03_TH34									S										S
VEGFA	Treehouse									C										C			D
VEGFC	PEDAYA							P
VEGFC	TCGA							T
VEGFC	TH03_TH34							S
VEGFC	Treehouse							C, D
WEE1	TCGA																						T
WEE1	Treehouse																						C, D

Detail table for all samples

table_output_name <- paste0("../results/detailed_treehouse_outliers_detected_relative_to_different_cohorts_", format(Sys.time(), "%Y_%m_%d-%H_%M_%S_%Y"), ".tsv")


outliers %>%
  rename(outlier_gene = gene) %>%
  mutate(original_comparison_cohort_name = comparison_cohort,
         comparison_cohort = update_cohort_strings(original_comparison_cohort_name)) %>%
  arrange(Sample_ID,
          outlier_gene, 
          comparison_cohort) %>%
  select(Dataset = Sample_ID,
         `Outlier gene` = outlier_gene,
         `Comparison cohort` = comparison_cohort,
         `Pathway support` = pathway_support) %>% 
  write_tsv(table_output_name)

this output was saved as ../results/detailed_treehouse_outliers_detected_relative_to_different_cohorts_2024_02_16-16_12_43_2024.tsv

sessionInfo()

## R version 4.2.1 (2022-06-23)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.2
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggVennDiagram_1.2.2 cowplot_1.1.1       gridExtra_2.3      
##  [4] kableExtra_1.3.4    khroma_1.10.0       janitor_2.1.0      
##  [7] forcats_0.5.2       stringr_1.5.0       dplyr_1.0.10       
## [10] purrr_0.3.5         readr_2.1.3         tidyr_1.2.1        
## [13] tibble_3.2.1        ggplot2_3.4.4       tidyverse_1.3.2    
## 
## loaded via a namespace (and not attached):
##  [1] fs_1.6.3            sf_1.0-9            lubridate_1.9.0    
##  [4] bit64_4.0.5         RColorBrewer_1.1-3  webshot_0.5.4      
##  [7] httr_1.4.4          tools_4.2.1         backports_1.4.1    
## [10] bslib_0.5.0         utf8_1.2.3          R6_2.5.1           
## [13] KernSmooth_2.23-20  lazyeval_0.2.2      DBI_1.1.3          
## [16] colorspace_2.1-0    withr_2.5.0         tidyselect_1.2.0   
## [19] bit_4.0.5           compiler_4.2.1      cli_3.6.1          
## [22] rvest_1.0.3         xml2_1.3.3          plotly_4.10.1      
## [25] labeling_0.4.2      sass_0.4.7          scales_1.2.1       
## [28] classInt_0.4-9      proxy_0.4-27        systemfonts_1.0.4  
## [31] digest_0.6.33       yulab.utils_0.0.6   rmarkdown_2.23     
## [34] svglite_2.1.0       pkgconfig_2.0.3     htmltools_0.5.5    
## [37] dbplyr_2.2.1        fastmap_1.1.1       highr_0.10         
## [40] htmlwidgets_1.6.2   rlang_1.1.1         readxl_1.4.1       
## [43] rstudioapi_0.14     jquerylib_0.1.4     farver_2.1.1       
## [46] generics_0.1.3      jsonlite_1.8.7      crosstalk_1.2.0    
## [49] vroom_1.6.0         googlesheets4_1.0.1 magrittr_2.0.3     
## [52] Rcpp_1.0.11         munsell_0.5.0       fansi_1.0.4        
## [55] lifecycle_1.0.3     stringi_1.7.12      yaml_2.3.7         
## [58] snakecase_0.11.0    grid_4.2.1          parallel_4.2.1     
## [61] crayon_1.5.2        haven_2.5.1         hms_1.1.2          
## [64] knitr_1.43          pillar_1.9.0        reprex_2.0.2       
## [67] glue_1.6.2          evaluate_0.21       data.table_1.14.6  
## [70] modelr_0.1.10       vctrs_0.6.3         tzdb_0.3.0         
## [73] cellranger_1.1.0    gtable_0.3.3        assertthat_0.2.1   
## [76] cachem_1.0.8        xfun_0.39           broom_1.0.1        
## [79] e1071_1.7-13        class_7.3-20        googledrive_2.0.0  
## [82] RVenn_1.1.0         viridisLite_0.4.2   gargle_1.2.1       
## [85] units_0.8-1         timechange_0.1.1    ellipsis_0.3.2